class_docstr: A multi-head masked self-attention layer with a projection at the end.
code: def __init__(
      self, config: GPTConfig, dtype: torch.dtype = torch.float32
  ) -> None:
      super().__init__()
      assert config.n_embd % config.n_head == 0
      self.resid_drop = nn.Dropout(config.resid_pdrop)
      self.c_proj = nn.Linear(
          config.n_embd,
          config.n_embd,
          device=config.device,
          dtype=dtype,
      )
      self.register_buffer(
          "mask",
          torch.tril(
              torch.ones(config.block_size, config.block_size)
          ).view(1, 1, config.block_size, config.block_size),
      )
      self.attn = torch.nn.MultiheadAttention(
          embed_dim=config.n_embd,
          num_heads=config.n_head,
          dropout=config.attn_pdrop,
          batch_first=True,
          device=config.device,
          dtype=dtype,
      )
  
imports: import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new MultiheadAttentionLayer object from inherited base classes: nn with example: obj = MultiheadAttentionLayer(config, dtype)